In this article, we introduce a novel community detection approach, multihop NMF (MHNMF), that explicitly considers the multihop connectivity structure of a network. Later, we introduce a performant algorithm for optimizing MHNMF, supported by a detailed theoretical evaluation of its computational complexity and convergence rate. The performance of MHNMF on 12 actual benchmark networks was assessed against 12 existing community detection methods, demonstrating that MHNMF is superior in performance.

Drawing inspiration from the human visual system's global-local information processing, we present a novel convolutional neural network (CNN) architecture, CogNet, comprised of a global pathway, a local pathway, and a top-down modulation component. We initially utilize a prevalent CNN block to construct the local pathway that aims to extract fine-grained local characteristics from the input image. The global pathway, capturing global structural and contextual information from local parts within the input image, is then derived using a transformer encoder. In conclusion, we create a learnable top-down modulator, adapting the specific local characteristics of the local pathway through the use of global representations from the global pathway. For the sake of user-friendliness, we encapsulate the dual-pathway computation and modulation process within a modular component, termed the global-local block (GL block). A CogNet of any desired depth can be constructed by sequentially integrating a suitable quantity of GL blocks. Rigorous testing of the proposed CogNets on six benchmark datasets demonstrates their unparalleled performance, surpassing all existing models and successfully addressing texture bias and semantic ambiguity common in CNN architectures.

To determine human joint torques while walking, inverse dynamics is a frequently employed technique. Traditional approaches require measurements of ground reaction force and kinematics for subsequent analysis. This work proposes a novel real-time hybrid methodology, integrating a neural network with a dynamic model, and leveraging exclusively kinematic data. An end-to-end neural network model is created to calculate joint torques directly, employing kinematic data as input. Neural networks are educated on diverse walking conditions, including the start and stop sequences, sudden alterations in pace, and the distinctive characteristic of asymmetrical movement. A detailed dynamic gait simulation (OpenSim) is initially employed to evaluate the hybrid model, yielding root mean square errors below 5 N.m and a correlation coefficient exceeding 0.95 for all joints. Comparative analyses of experimental data reveal that the end-to-end model, on average, exhibits better performance than the hybrid model throughout the entire testing procedure, when benchmarking against the gold standard method, which relies on both kinetic and kinematic information. The two torque estimators were similarly tested on a single participant utilizing a lower limb exoskeleton. The hybrid model (R>084) decisively outperforms the end-to-end neural network (R>059) in terms of performance in this instance. immunity effect The hybrid model's suitability is highlighted by its effectiveness in situations deviating from the training data.

Stroke, heart attack, and even sudden death can stem from the unchecked thromboembolism that occurs within blood vessels. Effective thromboembolism treatment has been shown through sonothrombolysis, significantly boosted by ultrasound contrast agents. Sonothrombolysis, performed intravascularly, has shown potential as a recent development for treating deep vein thrombosis, making it potentially effective and safe. Despite the positive treatment outcomes, the potential for optimized clinical application efficiency remains constrained by the lack of imaging guidance and clot characterization during the thrombolysis. A miniaturized intravascular sonothrombolysis transducer, constructed from an 8-layer PZT-5A stack having a 14×14 mm² aperture, was designed and assembled into a custom two-lumen 10-Fr catheter, as detailed in this paper. II-PAT, a hybrid imaging modality, monitored the treatment, leveraging the distinctive contrast from optical absorption and the extensive depth of ultrasound detection. II-PAT's innovative approach to intravascular light delivery, utilizing a thin optical fiber integrated with the catheter, effectively overcomes the limitations in tissue penetration depth arising from significant optical attenuation. PAT-guided in-vitro sonothrombolysis experiments involved synthetic blood clots, which were placed within a tissue phantom. At a clinically significant depth of ten centimeters, II-PAT can estimate the oxygenation level, shape, stiffness, and position of clots. epigenetic biomarkers Our findings reveal the feasibility of the proposed PAT-guided intravascular sonothrombolysis, with a real-time feedback mechanism actively implemented during the treatment.

Employing dual-energy spectral CT (DECT), this study presents a computer-aided diagnosis (CADx) framework, CADxDE, that directly processes transmission data within the pre-log domain to extract spectral information for improved lesion diagnosis. The CADxDE's functionality includes material identification and machine learning (ML) based CADx applications. The advantages of DECT's virtual monoenergetic imaging, focused on identified materials, permit machine learning to analyze how different tissue types (muscle, water, fat) respond within lesions at each energy level, for the purpose of computer-aided diagnosis (CADx). A pre-log domain model-based iterative reconstruction process is implemented to derive decomposed material images from DECT scans, thereby maintaining essential scan details. These decomposed images are then utilized to generate virtual monoenergetic images (VMIs) at chosen energies, n. In spite of the identical anatomy across these VMIs, their contrast distribution patterns, in conjunction with n-energies, provide considerable insight into tissue characterization. For this purpose, an ML-based CADx system is constructed to take advantage of the energy-heightened tissue attributes for the purpose of identifying malignant and benign lesions. check details An innovative multi-channel 3D convolutional neural network (CNN) approach, operating on original images and utilizing machine learning (ML) methods based on extracted lesion features, is designed to showcase the viability of CADxDE. Clinical datasets with pathologic confirmation yielded AUC scores 401% to 1425% greater than conventional DECT (high and low energy) and CT data. The diagnostic performance of lesions saw a substantial boost, exceeding 913% in the mean AUC scores, thanks to the energy spectral-enhanced tissue features from CADxDE.

Whole-slide image (WSI) classification, a critical component of computational pathology, faces significant hurdles, stemming from the high resolution, the expense of manual annotation, and the complexity arising from diverse data sources. Inherently, the gigapixel high resolution of whole-slide images (WSIs) poses a significant memory bottleneck for multiple instance learning (MIL) approaches to classification. Due to this limitation, most existing MIL network solutions require separating the feature encoder from the MIL aggregator, potentially significantly affecting performance. This paper's Bayesian Collaborative Learning (BCL) framework aims to resolve the memory bottleneck challenge presented by WSI classification. Our design incorporates an auxiliary patch classifier to work alongside the target MIL classifier. This integration facilitates simultaneous learning of the feature encoder and the MIL aggregator within the MIL classifier, effectively overcoming the memory limitation. A collaborative learning procedure, based on a unified Bayesian probabilistic framework, is constructed, and a principled Expectation-Maximization algorithm is used to iteratively deduce the optimal model parameters. A quality-aware pseudo-labeling strategy, effective as an implementation of the E-step, is also proposed. Three public WSI datasets—CAMELYON16, TCGA-NSCLC, and TCGA-RCC—were employed to evaluate the proposed BCL. The resulting AUC values, 956%, 960%, and 975%, demonstrably outperform all compared methods. The method's complexities will be examined thoroughly and discussed extensively to further illuminate its application. For prospective work, we have made our source code accessible at https://github.com/Zero-We/BCL.

Thorough anatomical characterization of head and neck vasculature is imperative for the accurate diagnosis of cerebrovascular conditions. Despite advancements, the automatic and accurate labeling of vessels in computed tomography angiography (CTA), particularly in the head and neck, remains problematic due to the tortuous and branched nature of the vessels and their proximity to other vasculature. These challenges necessitate a new topology-aware graph network (TaG-Net) designed specifically for vessel labeling. It fuses the advantages of volumetric image segmentation in voxel space with centerline labeling in line space, utilizing the voxel space for detailed local information and the line space for high-level anatomical and topological data extracted from the vascular graph based on centerlines. By extracting centerlines from the initial vessel segmentations, we establish a vascular graph. The next step involves labeling vascular graphs via TaG-Net, integrating topology-preserving sampling, topology-aware feature grouping, and multi-scale vascular graph structures. In the subsequent step, the labeled vascular graph is utilized to augment the accuracy of volumetric segmentation by completing vessel structures. The final step involves labeling the head and neck vessels of 18 segments, achieved by applying centerline labels to the refined segmentation. Our method, applied to CTA images from a group of 401 subjects, demonstrated superior performance in vessel segmentation and labeling tasks compared with leading contemporary methods.

Real-time inference is a key motivating factor in the growing popularity of regression-based methods for multi-person pose estimation.